Abstract:

With the increasing sophistication and diversification of cyber attack methods, traditional security defenses face a significant challenge in accurately identifying malicious traffic. This study addresses common issues in malicious traffic detection, such as numerous ineffective features, data imbalance, and the complexity of attack methods, by developing an efficient detection method. The main contributions include: proposing a data cleansing and Firstly, this paper balancing technique to effectively enhance the quality of traffic feature data; Secondly, innovatively the combination of a simple recurrent neural network with a multi-head attention mechanism, enabled proposed the detection model to precisely handle sequential data, effectively captured and identified various types of information and their dependencies, thereby significantly improved the accuracy of feature extraction; Finaly, the advantages of ensemble learning, deep learning, and machine learned to enable the detection model to efficiently learn from limited samples and quickly adapt to different network characteristics. Through experimental validation, this method demonstrates prominent detection performance on multiple public datasets.

Key words: malicious traffic detection, RNN, feature extraction, ensemble learning